{
  "abstract": "Conventional laser frequency stabilization techniques such as table-lookup or heuristic methods have significant limitations due to intrinsic hysteresis and stochastic mode competition. To overcome this challenge, we introduce a simple hysteresis-aware protocol and a tailored image-based reinforcement learning (RL) framework. This approach enables robust mode hop recovery in external-cavity diode lasers (ECDLs). Using a Rainbow deep Q-network (DQN) trained entirely in simulation, our RL agent achieves 100% recovery success rate across all 163 continuous temperature drifts produced under deliberately extreme perturbations during a three-day stress test. Under challenging discrete test conditions with abrupt current jumps, the success probability reaches 99.9%, with only one failure recorded out of one thousand trials. The controller generalizes well across environmental perturbations, such as temperature fluctuations, without requiring additional calibration. Our approach provides real-time adaptive control with minimal human intervention, marking a step toward fully autonomous operation in quantum experiments and precision optical systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Jaeick Bae"
    },
    {
      "name": "Kyoungsik Yu"
    },
    {
      "name": "Yunheung Song",
      "orcid": "0000-0001-9742-2688"
    },
    {
      "name": "Jeong Ho Han"
    },
    {
      "name": "Jongchul Mun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6w3rks6byx836.bib",
  "cited_by": 0,
  "doi": "10.1038/s42005-026-02931-x",
  "fulltext_url": null,
  "id": "6w3rks6byx836",
  "issns": [
    "2399-3650"
  ],
  "landing_url": "https://www.nature.com/articles/s42005-026-02931-x",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2208|Semiconductor Lasers and Optical Devices"
    ]
  ],
  "openalex": "W7213751945",
  "pdf_url": "https://www.nature.com/articles/s42005-026-02931-x_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/6w3rks6byx836.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:52Z",
      "native_id": "10.1038/s42005-026-02931-x",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s42005-026-02931-x"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "journal-openalex:3107|Topological Materials and Phenomena",
      "subfield": "amo"
    }
  ],
  "title": "Image-based reinforcement learning for robust mode hop recovery in external-cavity diode lasers",
  "title_text": "Image-based reinforcement learning for robust mode hop recovery in external-cavity diode lasers",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:52Z",
  "url": "https://research.draconitic.com/paper/6w3rks6byx836",
  "venue": "Communications Physics",
  "versions": []
}